Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

Overview Tools Metrics About the catalogue

CatBoost

Github

Website | Documentation | Tutorials | Installation | Release Notes

CatBoost is a machine learning method based on gradient boosting over decision trees.

Main advantages of CatBoost:

Superior quality when compared with other GBDT libraries on many datasets.
Best in class prediction speed.
Support for both numerical and categorical features.
Fast GPU and multi-GPU support for training out of the box.
Visualization tools included.
Fast and reproducible distributed training with Apache Spark and CLI.

Get Started and Documentation

All CatBoost documentation is available here.

Install CatBoost by following the guide for the

Next you may want to investigate:

Tutorials
Training modes and metrics
Cross-validation
Parameters tuning
Feature importance calculation
Regular and staged predictions
CatBoost for Apache Spark videos: Introduction and Architecture

If you cannot open documentation in your browser try adding yastatic.net and yastat.net to the list of allowed domains in your privacy badger.

Catboost models in production

If you want to evaluate Catboost model in your application read model api documentation.

Questions and bug reports

For reporting bugs please use the catboost/bugreport page.
Ask a question on Stack Overflow with the catboost tag, we monitor this for new questions.
Seek prompt advice at Telegram group or Russian-speaking Telegram chat

Help to Make CatBoost Better

Check out open problems and help wanted issues to see what can be improved, or open an issue if you want something.
Add your stories and experience to Awesome CatBoost.
To contribute to CatBoost you need to first read CLA text and add to your pull request, that you agree to the terms of the CLA. More information can be found in CONTRIBUTING.md
Instructions for contributors can be found here.

News

Reference Paper

Anna Veronika Dorogush, Andrey Gulin, Gleb Gusev, Nikita Kazeev, Liudmila Ostroumova Prokhorenkova, Aleksandr Vorobev “Fighting biases with dynamic boosting”. arXiv:1706.09516, 2017.

Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin “CatBoost: gradient boosting with categorical features support”. Workshop on ML Systems at NIPS 2017.

License

About the tool

You can click on the links to see the associated tools

Developing organisation(s):

catboost

Tool type(s):

Toolkit/software

Type of approach:

Technical
Educational

Maturity:

Implemented in multiple projects
In development

Usage rights:

Open source/Permissive

License:

Apache License 2.0

Programming languages:

Assembly
Batchfile
C
C#
C++
Clean
CMake
CSS
Cuda
Cython

Github stars:

6423

Github forks:

Modify this tool

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.